The present invention relates to Coordinated Multi-Point (CoMP) transmission in wireless communications and more particularly to interference suppression for CoMP transmission.
CoMP transmission is an emerging technology that can suppress interference and improve the capacity of multi-cell wireless networks. However, existing CoMP techniques either require sharing of data and channel-state information (CSI) for all links in the network, or have limited capability of interference suppression.
By allowing information sharing and cooperation between transmitters, CoMP transmission can drastically reduce inter-cell interference, thereby improving the capacity of an entire network. Its advantages have been validated both theoretically and experimentally.
CoMP transmission requires the transmitters to share CSI. However, CSI-sharing only has a limited advantage in suppressing interference. By augmenting data sharing, more interference between neighboring links can be removed.
For example, when using zero-forcing (ZF) precoding, multiple links are combined into one multiple-input multiple-output (MIMO) transmission via CSI/data sharing of transmitters, and each link can transmit as if there is no mutual interference. However, such a scheme may need to group links into clusters Links near the cluster edge still suffer from interference with neighboring clusters. To reduce such an edge effect and enable concurrent transmission of many links, links of the entire network may need to be grouped and cooperate with each other. This situation may be unrealistic for large networks, due to limited capacity of the backhaul network used for information sharing among the links.
The following references are related to interference suppression in CoMP transmission:                [1] Sawahashi, M. and Kishiyama, Y. and Morimoto, A. and Nishikawa, D. and Tanno, M., “Coordinated Multipoint Transmission/Reception Techniques for LTE-Advanced [Coordinated and Distributed MIMO],” IEEE Wireless Communications, vol. 17, no. 3, 2010.        [2] V. Jungnickel, A. Forck, S. Jaeckel, F. Bauermeister, S. Schiffermueller, S. Schubert, S. Wahls, L. Thiele, T. Haustein, W. Kreher, J. Mueller, H. Droste, and G. Kadel, “Field Trials Using Coordinated Multi-Point Transmission in the Downlink,” in IEEE PIMRC Workshops, 2010.        [3] S. Gollakota, S. D. Perli, and D. Katabi, “Interference Alignment and Cancellation,” in Proc. of ACM SIGCOMM, 2009.        [4] V. Cadambe and S. Jafar, “Interference Alignment and Degrees of Freedom of the K-User Interference Channel,” IEEE Transactions on Information Theory, vol. 54, no. 8, 2008.        [5] K. S. Gomadam, V. R. Cadambe, and S. A. Jafar, “Approaching the Capacity of Wireless Networks through Distributed Interference Alignment,” CoRR, vol. abs/0803.3816, 2008.        [6] S. Gollakota, S. D. Perli, and D. Katabi, “Interference Alignment and Cancellation,” in Proc. of ACM SIGCOMM, 2009.        [7] K. Balachandran, J. Kang, K. Karakayali, and K. Rege, “NICE: A Network Interference Cancellation Engine for Opportunistic Uplink Cooperation in Wireless Networks,” IEEE Transactions on Wireless Communications, vol. 10, no. 2, 2011.        [8] M. Sadek, A. Tarighat, and A. Sayed, “A leakage-based precoding scheme for downlink multi-user mimo channels,” Wireless Communications, IEEE Transactions on, vol. 6, no. 5, 2007.        [9] A. Khattab, J. Camp, C. Hunter, P. Murphy, A. Sabharwal, and E. W. Knightly, “WARP: a Flexible Platform for Clean-Slate Wireless Medium Access Protocol Design,” SIGMOBILE Mob. Comput. Commun. Rev., vol. 12, 2008.        [10] D. Gesbert, S. Hanly, H. Huang, S. Shamai Shitz, O. Simeone, and W. Yu, “Multi-Cell MIMO Cooperative Networks: A New Look at Interference,” IEEE Journal on Selected Areas in Communications (JSAC), vol. 28, no. 9, 2010.        [11] M. Guillaud and D. Gesbert, “Interference Alignment In The Partially Connected K-User MIMO Interference Channel,” in Proc. of European Signal Processing Conference (EUSIPCO), 2011.        [12] K. Jamieson, “The SoftPHY Abstraction: from Packets to Symbols in Wireless Network Design,” Ph.D. Thesis, MIT, 2008.        
CoMP transmission may take many forms, depending on the scale of cooperation (e.g., intra-cell or inter-cell), the information to be shared (e.g., CSI sharing or both CSI and data sharing), etc. [1]. As mentioned above, the existing CoMP schemes may need to cluster links, and links near the cluster edge may severely interfere with other links. To increase the degrees of freedom in the network (e.g., the number of concurrent transmissions), such schemes may need to increase the cluster size accordingly, which is impractical due to the formidable overhead in delivering all the shared data and CSI.
MIMO stream control [3] is an alternative way of improving the degrees of freedom. Given the channel matrix between interferers/transmitters and the receiver, stream control is able to suppress (N−1) interferers and receive 1 useful stream of data, assuming there are N antennas. However, stream control is not scalable because its achievable degrees of freedom strongly depend on the number of antennas, which is limited in practice.
In [8], a distributed algorithm that suppresses the leakage interference to neighboring links by maximizing the signal-to-leakage-and-noise ratio (SLNR) is proposed. We will show that the distributed algorithm (hereafter referred to as max SLNR) may be a special case of distributed interference alignment, and explain the factors behind its low performance compared with other schemes.
Balachandran et al. [7] proposed a network cancellation algorithm for the uplink of CoMP systems. The basic idea is to allow links that can decode their frames to send the decoded packets to other links, which then cancel such known interference using successive interference cancellation (SIC). In distributed interference alignment and cancellation (DIAC), the localized uplink cancellation works in a similar way, but is integrated with interference alignment that substantially improves the interference suppression capability. In addition, we design a distributed, DPC (dirty-paper-coding)-based algorithm that is applicable to the downlink of multi-cell networks.
Interference alignment [4] may be a promising mechanism for improving the network degree of freedom. In theory, it can achieve MK/2 total degrees of freedom when there are K links in the network each with M×M MIMO (assuming half-duplex radios), i.e., half of the links can transmit concurrently. However, this ideal bound is achievable when the channel is highly dynamic, e.g., when the channel state changes over each symbol, which conversely renders channel estimation infeasible. In practice, interference alignment can be realized by designing the precoding matrix at the transmitter and the projection matrix at the receiver. The matrix design is equivalent to an over-constrained system of equations, and typically a subset of the constraints can be satisfied. Equivalently, a limited number of interferers can be suppressed. In DIAC, we adopt a similar approach of matrix design, but integrate it with interference cancellation, thus further improving the total degrees of freedom in the network.
In [6], Gollakota et al. implemented a preliminary version of interference alignment, and integrated interference alignment with SIC on the uplink to improve the number of concurrent transmissions. However, [6] is applicable for a single collision domain (e.g., where all links interfere with each other), and is not scalable in large wireless networks. In fact, the scheme in [6] can tolerate at most 2M concurrent uplink transmissions when each node has M antennas (and even fewer for the downlink), i.e., the degrees of freedom is eventually limited by the number of antennas on each node. In DIAC, by leveraging the locality of interference, it is possible to allow all links in a network to transmit concurrently even with a limited number of antennas.
We propose distributed interference alignment and cancellation (DIAC) to overcome these limitations. DIAC builds on a key intuition of interference locality—since each link interferes with a limited number of neighboring links, it is sufficient to coordinate with those strong interferences and ignore others, in order to limit the overhead in CoMP. DIAC realizes the localized coordination by integrating interference cancellation and distributed interference alignment, and can be applied to both the uplink and downlink of multi-cell wireless networks. To validate DIAC, we use both model-driven simulation and trace-based simulation where the traces are collected by implementing a MIMO-OFDM channel estimator on a software radio platform. Our experiments show that DIAC can substantially improve the degrees of freedom in multi-cell wireless networks.